Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes

Tanya Grancharova, Kaytlyn A. Gerbin, Alexander B. Rosenberg, Charles M. Roco, Joy Arakaki, Colette DeLizzo, Stephanie Q. Dinh, Rory Donovan-Maiye, Matthew Hirano, Angelique Nelson, Joyce Tang, Julie A. Theriot, Calysta Yan, Vilas Menon, Sean P. Palecek, Georg Seelig, Ruwanthi N. Gunawardane
doi: https://doi.org/10.1101/2021.04.22.441027
Tanya Grancharova
2Allen Institute for Cell Science, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kaytlyn A. Gerbin
2Allen Institute for Cell Science, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alexander B. Rosenberg
3Department of Electrical & Computer Engineering, University of Washington, Seattle, WA
4Parse Biosciences, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Charles M. Roco
3Department of Electrical & Computer Engineering, University of Washington, Seattle, WA
4Parse Biosciences, Seattle, WA
5Department of Bioengineering, University of Washington, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Joy Arakaki
2Allen Institute for Cell Science, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Colette DeLizzo
2Allen Institute for Cell Science, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stephanie Q. Dinh
2Allen Institute for Cell Science, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rory Donovan-Maiye
2Allen Institute for Cell Science, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Matthew Hirano
3Department of Electrical & Computer Engineering, University of Washington, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Angelique Nelson
2Allen Institute for Cell Science, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Joyce Tang
2Allen Institute for Cell Science, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Julie A. Theriot
2Allen Institute for Cell Science, Seattle, WA
6Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Calysta Yan
2Allen Institute for Cell Science, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Vilas Menon
7Department of Neurology, Columbia University Irving Medical Center, New York, NY
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sean P. Palecek
8Department of Chemical and Biological Engineering, University of Wisconsin - Madison, Madison, WI
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Georg Seelig
3Department of Electrical & Computer Engineering, University of Washington, Seattle, WA
9Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ruwanthi N. Gunawardane
2Allen Institute for Cell Science, Seattle, WA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: rug@alleninstitute.org
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

We performed a comprehensive analysis of the transcriptional changes within and across cell populations during human induced pluripotent stem cell (hiPSC) differentiation to cardiomyocytes. Using the single cell RNA-seq combinatorial barcoding method SPLiT-seq, we sequenced >20,000 single cells from 55 independent samples representing two differentiation protocols and multiple hiPSC lines. Samples included experimental replicates ranging from undifferentiated hiPSCs to mixed populations of cells at D90 post-differentiation. As expected, differentiated cell populations clustered by time point, with differential expression analysis revealing markers of cardiomyocyte differentiation and maturation changing from D12 to D90. We next performed a complementary cluster-independent sparse regression analysis to identify and rank genes that best assigned cells to differentiation time points. The two highest ranked genes between D12 and D24 (MYH7 and MYH6) resulted in an accuracy of 0.84, and the three highest ranked genes between D24 and D90 (A2M, H19, IGF2) resulted in an accuracy of 0.94, revealing that low dimensional gene features can identify differentiation or maturation stages in differentiating cardiomyocytes. Expression levels of select genes were validated using RNA FISH. Finally, we interrogated differences in differentiation population composition and cardiac gene expression resulting from two differentiation protocols, experimental replicates, and three hiPSC lines in the WTC-11 background to identify sources of variation across these experimental variables.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://open.quiltdata.com/b/allencell/packages/aics/wtc11_hipsc_cardiomyocyte_scrnaseq_d0_to_d90

  • https://github.com/AllenCell/cardio_scrnaseq_paper_code

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted April 23, 2021.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes
Tanya Grancharova, Kaytlyn A. Gerbin, Alexander B. Rosenberg, Charles M. Roco, Joy Arakaki, Colette DeLizzo, Stephanie Q. Dinh, Rory Donovan-Maiye, Matthew Hirano, Angelique Nelson, Joyce Tang, Julie A. Theriot, Calysta Yan, Vilas Menon, Sean P. Palecek, Georg Seelig, Ruwanthi N. Gunawardane
bioRxiv 2021.04.22.441027; doi: https://doi.org/10.1101/2021.04.22.441027
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes
Tanya Grancharova, Kaytlyn A. Gerbin, Alexander B. Rosenberg, Charles M. Roco, Joy Arakaki, Colette DeLizzo, Stephanie Q. Dinh, Rory Donovan-Maiye, Matthew Hirano, Angelique Nelson, Joyce Tang, Julie A. Theriot, Calysta Yan, Vilas Menon, Sean P. Palecek, Georg Seelig, Ruwanthi N. Gunawardane
bioRxiv 2021.04.22.441027; doi: https://doi.org/10.1101/2021.04.22.441027

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Cell Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4095)
  • Biochemistry (8786)
  • Bioengineering (6493)
  • Bioinformatics (23386)
  • Biophysics (11766)
  • Cancer Biology (9167)
  • Cell Biology (13290)
  • Clinical Trials (138)
  • Developmental Biology (7422)
  • Ecology (11386)
  • Epidemiology (2066)
  • Evolutionary Biology (15119)
  • Genetics (10413)
  • Genomics (14024)
  • Immunology (9145)
  • Microbiology (22108)
  • Molecular Biology (8793)
  • Neuroscience (47445)
  • Paleontology (350)
  • Pathology (1423)
  • Pharmacology and Toxicology (2483)
  • Physiology (3711)
  • Plant Biology (8063)
  • Scientific Communication and Education (1433)
  • Synthetic Biology (2215)
  • Systems Biology (6021)
  • Zoology (1251)